A COMPARATIVE CLASSIFICATION STUDY ON THE USE OF AUDIBLE ACOUSTIC EMISSION SIGNALS FOR SURFACE ROUGHNESS CONDITION MONITORING IN SHOULDER MILLING OF STEEL

Despite the current advances in additive manufacturing, subtractive manufacturing methods such as machining still account for the major share of modern manufacturing. Surface roughness of a machined product is a crucial parameter that impacts the functionality, assembly, and service life of the product. Surface finish texture of machined components is too complex for accurate prediction when using analytical or computer simulation techniques. This is because there are numerous parameters relating to the material of the workpiece, cutting tool, and machining process conditions. Therefore, machine learning (ML) techniques are becoming more popular in creating model-based methods that are capable of providing more reliable real-time surface quality prediction. The aim of this study is to develop ML models to predict the surface roughness in shoulder milling of steel parts using audible acoustic emission data produced during machining. Microphones are used to pick up acoustic data that is highly correlated to acoustic waves produced by the machining process. These sound measuring devices are non-invasive and can be easily integrated within the machining envelope without disrupting or stopping the machining process. Features are then extracted from the acoustic data that include averaged wavelet decomposition quantities, statistical quantities, and filtered time signatures of the sound waves. These are then (1) used to training classifiers using important features or a combination of features that have highly corelated to surface finish, and (2) develop a learning model to use these features to predict the surface roughness in shoulder milling. In this study, we use a variety of dimensionality reduction algorithms in the training phase. In the learning model, we use classification algorithms and develop suitable classifiers. The overall objective is to develop a reliable and robust predictive tool with potential for practical implementation in a real-time industrial machine tool installation for process monitoring.